Please enter a description about the method here

The **ScaledSoftSign** is a modification of **[SoftSign](https://paperswithcode.com/method/softsign-activation)** activation function that has trainable parameters.

$$ScaledSoftSign(x) = \frac{\alpha x}{\beta + |x|}$$

ScaledSoftSign

Trainable Activations for Image Classification

Regularisation of Neural Networks by Enforcing Lipschitz Continuity

**TDN**, or **Temporaral Difference Network**, is an action recognition model that aims to capture multi-scale temporal information. To fully capture temporal information over the entire video, the TDN is established with a two-level difference modeling paradigm. Specifically, for local motion modeling, temporal difference over consecutive frames is used to supply 2D CNNs with finer motion pattern, while for global motion modeling, temporal difference across segments is incorporated to capture long-range structure for motion feature excitation.

Source	Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com